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Laymouna M, Ma Y, Lessard D, Schuster T, Engler K, Lebouché B. Roles, Users, Benefits, and Limitations of Chatbots in Health Care: Rapid Review. J Med Internet Res 2024; 26:e56930. [PMID: 39042446 PMCID: PMC11303905 DOI: 10.2196/56930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 04/07/2024] [Accepted: 04/12/2024] [Indexed: 07/24/2024] Open
Abstract
BACKGROUND Chatbots, or conversational agents, have emerged as significant tools in health care, driven by advancements in artificial intelligence and digital technology. These programs are designed to simulate human conversations, addressing various health care needs. However, no comprehensive synthesis of health care chatbots' roles, users, benefits, and limitations is available to inform future research and application in the field. OBJECTIVE This review aims to describe health care chatbots' characteristics, focusing on their diverse roles in the health care pathway, user groups, benefits, and limitations. METHODS A rapid review of published literature from 2017 to 2023 was performed with a search strategy developed in collaboration with a health sciences librarian and implemented in the MEDLINE and Embase databases. Primary research studies reporting on chatbot roles or benefits in health care were included. Two reviewers dual-screened the search results. Extracted data on chatbot roles, users, benefits, and limitations were subjected to content analysis. RESULTS The review categorized chatbot roles into 2 themes: delivery of remote health services, including patient support, care management, education, skills building, and health behavior promotion, and provision of administrative assistance to health care providers. User groups spanned across patients with chronic conditions as well as patients with cancer; individuals focused on lifestyle improvements; and various demographic groups such as women, families, and older adults. Professionals and students in health care also emerged as significant users, alongside groups seeking mental health support, behavioral change, and educational enhancement. The benefits of health care chatbots were also classified into 2 themes: improvement of health care quality and efficiency and cost-effectiveness in health care delivery. The identified limitations encompassed ethical challenges, medicolegal and safety concerns, technical difficulties, user experience issues, and societal and economic impacts. CONCLUSIONS Health care chatbots offer a wide spectrum of applications, potentially impacting various aspects of health care. While they are promising tools for improving health care efficiency and quality, their integration into the health care system must be approached with consideration of their limitations to ensure optimal, safe, and equitable use.
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Affiliation(s)
- Moustafa Laymouna
- Department of Family Medicine, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada
- Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
- Infectious Diseases and Immunity in Global Health Program, Research Institute of McGill University Health Centre, Montreal, QC, Canada
| | - Yuanchao Ma
- Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
- Infectious Diseases and Immunity in Global Health Program, Research Institute of McGill University Health Centre, Montreal, QC, Canada
- Chronic and Viral Illness Service, Division of Infectious Disease, Department of Medicine, McGill University Health Centre, Montreal, QC, Canada
- Department of Biomedical Engineering, Polytechnique Montréal, Montreal, QC, Canada
| | - David Lessard
- Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
- Infectious Diseases and Immunity in Global Health Program, Research Institute of McGill University Health Centre, Montreal, QC, Canada
- Chronic and Viral Illness Service, Division of Infectious Disease, Department of Medicine, McGill University Health Centre, Montreal, QC, Canada
| | - Tibor Schuster
- Department of Family Medicine, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada
| | - Kim Engler
- Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
- Infectious Diseases and Immunity in Global Health Program, Research Institute of McGill University Health Centre, Montreal, QC, Canada
- Chronic and Viral Illness Service, Division of Infectious Disease, Department of Medicine, McGill University Health Centre, Montreal, QC, Canada
| | - Bertrand Lebouché
- Department of Family Medicine, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada
- Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
- Infectious Diseases and Immunity in Global Health Program, Research Institute of McGill University Health Centre, Montreal, QC, Canada
- Chronic and Viral Illness Service, Division of Infectious Disease, Department of Medicine, McGill University Health Centre, Montreal, QC, Canada
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Krasovsky T, Weiss PL, Gafni-Lachter L, Kizony R, Gefen N. Hybrid approaches to allied health services for children and young people: a scoping review. J Neuroeng Rehabil 2024; 21:122. [PMID: 39030627 PMCID: PMC11264746 DOI: 10.1186/s12984-024-01401-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 06/11/2024] [Indexed: 07/21/2024] Open
Abstract
BACKGROUND Hybrid models that integrate both in-person and remote health services are increasingly recognized as a promising approach. Nevertheless, research that defines and characterizes these models in children and young people is scarce and essential for establishing guidelines for implementation of hybrid allied health services. This scoping review evaluates four key aspects of hybrid allied health services in children and young people: 1. definitions, 2. service characteristics, 3. outcome measures, and 4. results of hybrid allied health services. METHODS Six databases were searched: Medline (Ovid), Embase, CINHAL, Psycinfo, Cochrane CENTRAL, and Web of Science. Of the 9,868 studies potentially meeting the inclusion criteria, 49 studies focused on children and young people. Following full-text review, n = 21 studies were included. RESULTS Terminology used for hybrid allied health services varied across studies which targeted diverse clinical populations and varied in study design, type and frequency of remote and in-person treatments. Over 75% of cases used custom-written software, limiting scalability. All interventions started in-person, possibly to establish a therapeutic alliance and solve technological issues. Most hybrid allied health services (67%) were in mental health, while only a minority involved physical, occupational or speech therapy. The most common outcomes were feasibility and satisfaction, but tools used to measure them were inconsistent. Although 57% of studies demonstrated effectiveness of hybrid allied health services, none measured cost-effectiveness. DISCUSSION Despite the potential of hybrid allied health services for children and young people, the literature remains at a preliminary stage. Standardization of definitions and outcome measures, and clearer reporting of service characteristics and results would likely promote consolidation of hybrid allied health services in children and young people into clinical practice.
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Affiliation(s)
- Tal Krasovsky
- Department of Physical Therapy, Faculty of Social Welfare and Health Sciences, University of Haifa, 199 Abba Hushi Avenue, Haifa, 3498838, Israel.
- Department of Pediatric Rehabilitation, The Edmond & Lily Safra Children's Hospital, Sheba Medical Center, Ramat Gan, Israel.
| | - Patrice L Weiss
- Department of Occupational Therapy, Faculty of Social Welfare and Health Sciences, University of Haifa, Haifa, Israel
- The Helmsley Pediatric & Adolescent Rehabilitation Research Center, ALYN Hospital, Jerusalem, Israel
| | - Liat Gafni-Lachter
- Department of Occupational Therapy, Faculty of Social Welfare and Health Sciences, University of Haifa, Haifa, Israel
- College of Health and Rehabilitation Sciences, Department of Occupational Therapy, Sargent College, Boston University, Boston, USA
| | - Rachel Kizony
- Department of Occupational Therapy, Faculty of Social Welfare and Health Sciences, University of Haifa, Haifa, Israel
- Department of Occupational Therapy, Sheba Medical Center, Ramat Gan, Israel
| | - Naomi Gefen
- The Helmsley Pediatric & Adolescent Rehabilitation Research Center, ALYN Hospital, Jerusalem, Israel
- School of Occupational Therapy, Hebrew University, Jerusalem, Israel
- ALYN Hospital, Jerusalem, Israel
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Mukumbya B, Kitya D, Trillo-Ordonez Y, Sun K, Obiga O, Deng DD, Stewart KA, Ukachukwu AEK, Haglund MM, Fuller AT. The feasibility, appropriateness, and usability of mobile neuro clinics in addressing the neurosurgical and neurological demand in Uganda. PLoS One 2024; 19:e0305382. [PMID: 38913633 PMCID: PMC11195962 DOI: 10.1371/journal.pone.0305382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 05/28/2024] [Indexed: 06/26/2024] Open
Abstract
INTRODUCTION Uganda has a high demand for neurosurgical and neurological care. 78% of the over 50 million population reside in rural and remote communities where access to neurosurgical and neurological services is lacking. This study aimed to determine the feasibility, appropriateness, and usability of mobile neuro clinics (MNCs) in providing neurological care to rural and remote Ugandan populations. METHODS Neurosurgery, neurology, and mobile health clinic providers participated in an education and interview session to assess the feasibility, appropriateness, and usability of the MNC intervention. A qualitative analysis of the interview responses using the constructs in the updated Consolidated Framework for Implementation Research was performed. Providers' opinions were weighted using average sentiment scores on a novel sentiment-weighted scale adapted from the CFIR. A stakeholder analysis was also performed to assess the power and interest of the actors described by the participants. RESULTS Twenty-one healthcare providers completed the study. Participants discussed the potential benefits and concerns of MNCs as well as potential barriers and critical incidents that could jeopardize the intervention. Of the five CFIR domains evaluated, variables in the implementation process domain showed the highest average sentiment scores, followed by the implementation climate constructs, inner setting, innovation, and outer setting domains. Furthermore, many interested stakeholders were identified with diverse roles and responsibilities for implementing MNCs. These findings demonstrate that MNC innovation is feasible, appropriate, and usable. CONCLUSION The findings of this study support the feasibility, appropriateness, and usability of MNCs in Uganda. However, integration of this innovation requires careful planning and stakeholder engagement at all levels to ensure the best possible outcomes.
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Affiliation(s)
- Benjamin Mukumbya
- Duke Global Neurosurgery and Neurology, Durham, NC, United States of America
- Duke Global Health Institute, Durham, NC, United States of America
| | - David Kitya
- Duke Global Neurosurgery and Neurology, Durham, NC, United States of America
- Department of Neurosurgery, Mbarara Regional Referral Hospital, Mbarara, Uganda
- Mbarara University of Science and Technology, Mbarara, Uganda
| | - Yesel Trillo-Ordonez
- Duke Global Neurosurgery and Neurology, Durham, NC, United States of America
- Duke Global Health Institute, Durham, NC, United States of America
| | - Keying Sun
- Duke Global Health Institute, Durham, NC, United States of America
- Duke University School of Medicine, Durham, NC, United States of America
| | - Oscar Obiga
- Duke Global Neurosurgery and Neurology, Durham, NC, United States of America
- Department of Neurosurgery, Mulago National Referral Hospital, Kampala, Uganda
| | - Di D. Deng
- Duke Global Neurosurgery and Neurology, Durham, NC, United States of America
| | | | - Alvan-Emeka K. Ukachukwu
- Duke Global Neurosurgery and Neurology, Durham, NC, United States of America
- Duke Global Health Institute, Durham, NC, United States of America
- Department of Neurosurgery, Duke University Health System, Durham, NC, United States of America
| | - Michael M. Haglund
- Duke Global Neurosurgery and Neurology, Durham, NC, United States of America
- Duke Global Health Institute, Durham, NC, United States of America
- Department of Neurosurgery, Duke University Health System, Durham, NC, United States of America
| | - Anthony T. Fuller
- Duke Global Neurosurgery and Neurology, Durham, NC, United States of America
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MacNeill AL, MacNeill L, Yi S, Goudreau A, Luke A, Doucet S. Depiction of conversational agents as health professionals: a scoping review. JBI Evid Synth 2024; 22:831-855. [PMID: 38482610 DOI: 10.11124/jbies-23-00029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/09/2024]
Abstract
OBJECTIVE The purpose of this scoping review was to examine the depiction of conversational agents as health professionals. We identified the professional characteristics that are used with these depictions and determined the prevalence of these characteristics among conversational agents that are used for health care. INTRODUCTION The depiction of conversational agents as health professionals has implications for both the users and the developers of these programs. For this reason, it is important to know more about these depictions and how they are implemented in practical settings. INCLUSION CRITERIA This review included scholarly literature on conversational agents that are used for health care. It focused on conversational agents designed for patients and health seekers, not health professionals or trainees. Conversational agents that address physical and/or mental health care were considered, as were programs that promote healthy behaviors. METHODS This review was conducted in accordance with JBI methodology for scoping reviews. The databases searched included MEDLINE (PubMed), Embase, CINAHL with Full Text (EBSCOhost), Scopus, Web of Science, ACM Guide to Computing Literature (Association for Computing Machinery Digital Library), and IEEE Xplore (IEEE). The main database search was conducted in June 2021, and an updated search was conducted in January 2022. Extracted data included characteristics of the report, basic characteristics of the conversational agent, and professional characteristics of the conversational agent. Extracted data were summarized using descriptive statistics. Results are presented in a narrative summary and accompanying tables. RESULTS A total of 38 health-related conversational agents were identified across 41 reports. Six of these conversational agents (15.8%) had professional characteristics. Four conversational agents (10.5%) had a professional appearance in which they displayed the clothing and accessories of health professionals and appeared in professional settings. One conversational agent (2.6%) had a professional title (Dr), and 4 conversational agents (10.5%) were described as having professional roles. Professional characteristics were more common among embodied vs disembodied conversational agents. CONCLUSIONS The results of this review show that the depiction of conversational agents as health professionals is not particularly common, although it does occur. More discussion is needed on the potential ethical and legal issues surrounding the depiction of conversational agents as health professionals. Future research should examine the impact of these depictions, as well as people's attitudes toward them, to better inform recommendations for practice.
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Affiliation(s)
- A Luke MacNeill
- Centre for Research in Integrated Care, University of New Brunswick, Saint John, NB, Canada
- Department of Nursing and Health Sciences, University of New Brunswick, Saint John, NB, Canada
| | - Lillian MacNeill
- Centre for Research in Integrated Care, University of New Brunswick, Saint John, NB, Canada
- Department of Nursing and Health Sciences, University of New Brunswick, Saint John, NB, Canada
| | - Sungmin Yi
- Centre for Research in Integrated Care, University of New Brunswick, Saint John, NB, Canada
- College of Pharmacy, Dalhousie University, Halifax, NS, Canada
| | - Alex Goudreau
- University of New Brunswick Libraries, Saint John, NB, Canada
- The University of New Brunswick (UNB) Saint John Collaboration for Evidence-Informed Healthcare: A JBI Centre of Excellence, Saint John, NB, Canada
| | - Alison Luke
- Centre for Research in Integrated Care, University of New Brunswick, Saint John, NB, Canada
- Department of Nursing and Health Sciences, University of New Brunswick, Saint John, NB, Canada
- The University of New Brunswick (UNB) Saint John Collaboration for Evidence-Informed Healthcare: A JBI Centre of Excellence, Saint John, NB, Canada
| | - Shelley Doucet
- Centre for Research in Integrated Care, University of New Brunswick, Saint John, NB, Canada
- Department of Nursing and Health Sciences, University of New Brunswick, Saint John, NB, Canada
- The University of New Brunswick (UNB) Saint John Collaboration for Evidence-Informed Healthcare: A JBI Centre of Excellence, Saint John, NB, Canada
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Wang JW, Zhu Z, Shuling Z, Fan J, Jin Y, Gao ZL, Chen WD, Li X. Effectiveness of mHealth App-Based Interventions for Increasing Physical Activity and Improving Physical Fitness in Children and Adolescents: Systematic Review and Meta-Analysis. JMIR Mhealth Uhealth 2024; 12:e51478. [PMID: 38687568 PMCID: PMC11094610 DOI: 10.2196/51478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 02/11/2024] [Accepted: 03/14/2024] [Indexed: 05/02/2024] Open
Abstract
BACKGROUND The COVID-19 pandemic has significantly reduced physical activity (PA) levels and increased sedentary behavior (SB), which can lead to worsening physical fitness (PF). Children and adolescents may benefit from mobile health (mHealth) apps to increase PA and improve PF. However, the effectiveness of mHealth app-based interventions and potential moderators in this population are not yet fully understood. OBJECTIVE This study aims to review and analyze the effectiveness of mHealth app-based interventions in promoting PA and improving PF and identify potential moderators of the efficacy of mHealth app-based interventions in children and adolescents. METHODS We searched for randomized controlled trials (RCTs) published in the PubMed, Web of Science, EBSCO, and Cochrane Library databases until December 25, 2023, to conduct this meta-analysis. We included articles with intervention groups that investigated the effects of mHealth-based apps on PA and PF among children and adolescents. Due to high heterogeneity, a meta-analysis was conducted using a random effects model. The Cochrane Risk of Bias Assessment Tool was used to evaluate the risk of bias. Subgroup analysis and meta-regression analyses were performed to identify potential influences impacting effect sizes. RESULTS We included 28 RCTs with a total of 5643 participants. In general, the risk of bias of included studies was low. Our findings showed that mHealth app-based interventions significantly increased total PA (TPA; standardized mean difference [SMD] 0.29, 95% CI 0.13-0.45; P<.001), reduced SB (SMD -0.97, 95% CI -1.67 to -0.28; P=.006) and BMI (weighted mean difference -0.31 kg/m2, 95% CI -0.60 to -0.01 kg/m2; P=.12), and improved muscle strength (SMD 1.97, 95% CI 0.09-3.86; P=.04) and agility (SMD -0.35, 95% CI -0.61 to -0.10; P=.006). However, mHealth app-based interventions insignificantly affected moderate to vigorous PA (MVPA; SMD 0.11, 95% CI -0.04 to 0.25; P<.001), waist circumference (weighted mean difference 0.38 cm, 95% CI -1.28 to 2.04 cm; P=.65), muscular power (SMD 0.01, 95% CI -0.08 to 0.10; P=.81), cardiorespiratory fitness (SMD -0.20, 95% CI -0.45 to 0.05; P=.11), muscular endurance (SMD 0.47, 95% CI -0.08 to 1.02; P=.10), and flexibility (SMD 0.09, 95% CI -0.23 to 0.41; P=.58). Subgroup analyses and meta-regression showed that intervention duration was associated with TPA and MVPA, and age and types of intervention was associated with BMI. CONCLUSIONS Our meta-analysis suggests that mHealth app-based interventions may yield small-to-large beneficial effects on TPA, SB, BMI, agility, and muscle strength in children and adolescents. Furthermore, age and intervention duration may correlate with the higher effectiveness of mHealth app-based interventions. However, due to the limited number and quality of included studies, the aforementioned conclusions require validation through additional high-quality research. TRIAL REGISTRATION PROSPERO CRD42023426532; https://tinyurl.com/25jm4kmf.
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Affiliation(s)
- Jun-Wei Wang
- School of Sport Medicine and Health, Chengdu Sport University, Chengdu, China
- School of Sports Science, Beijing Sport University, Beijing, China
| | - Zhicheng Zhu
- Physical education institute, Xinyu University, Xinyu, China
| | - Zhang Shuling
- School of Sport Medicine and Health, Chengdu Sport University, Chengdu, China
| | - Jia Fan
- School of Sport Medicine and Health, Chengdu Sport University, Chengdu, China
| | - Yu Jin
- School of Sport Medicine and Health, Chengdu Sport University, Chengdu, China
| | - Zhan-Le Gao
- School of Sport Medicine and Health, Chengdu Sport University, Chengdu, China
| | - Wan-Di Chen
- Academic Administration, Chengdu Sport University, Chengdu, China
| | - Xue Li
- School of Sport Medicine and Health, Chengdu Sport University, Chengdu, China
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Metzendorf MI, Wieland LS, Richter B. Mobile health (m-health) smartphone interventions for adolescents and adults with overweight or obesity. Cochrane Database Syst Rev 2024; 2:CD013591. [PMID: 38375882 PMCID: PMC10877670 DOI: 10.1002/14651858.cd013591.pub2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
BACKGROUND Obesity is considered to be a risk factor for various diseases, and its incidence has tripled worldwide since 1975. In addition to potentially being at risk for adverse health outcomes, people with overweight or obesity are often stigmatised. Behaviour change interventions are increasingly delivered as mobile health (m-health) interventions, using smartphone apps and wearables. They are believed to support healthy behaviours at the individual level in a low-threshold manner. OBJECTIVES To assess the effects of integrated smartphone applications for adolescents and adults with overweight or obesity. SEARCH METHODS We searched CENTRAL, MEDLINE, PsycINFO, CINAHL, and LILACS, as well as the trials registers ClinicalTrials.gov and World Health Organization International Clinical Trials Registry Platform on 2 October 2023 (date of last search for all databases). We placed no restrictions on the language of publication. SELECTION CRITERIA Participants were adolescents and adults with overweight or obesity. Eligible interventions were integrated smartphone apps using at least two behaviour change techniques. The intervention could target physical activity, cardiorespiratory fitness, weight loss, healthy diet, or self-efficacy. Comparators included no or minimal intervention (NMI), a different smartphone app, personal coaching, or usual care. Eligible studies were randomised controlled trials of any duration with a follow-up of at least three months. DATA COLLECTION AND ANALYSIS We used standard Cochrane methodology and the RoB 2 tool. Important outcomes were physical activity, body mass index (BMI) and weight, health-related quality of life, self-efficacy, well-being, change in dietary behaviour, and adverse events. We focused on presenting studies with medium- (6 to < 12 months) and long-term (≥ 12 months) outcomes in our summary of findings table, following recommendations in the core outcome set for behavioural weight management interventions. MAIN RESULTS We included 18 studies with 2703 participants. Interventions lasted from 2 to 24 months. The mean BMI in adults ranged from 27 to 50, and the median BMI z-score in adolescents ranged from 2.2 to 2.5. Smartphone app versus no or minimal intervention Thirteen studies compared a smartphone app versus NMI in adults; no studies were available for adolescents. The comparator comprised minimal health advice, handouts, food diaries, smartphone apps unrelated to weight loss, and waiting list. Measures of physical activity: at 12 months' follow-up, a smartphone app compared to NMI probably reduces moderate to vigorous physical activity (MVPA) slightly (mean difference (MD) -28.9 min/week (95% confidence interval (CI) -85.9 to 28; 1 study, 650 participants; moderate-certainty evidence)). We are very uncertain about the results of estimated energy expenditure and cardiorespiratory fitness at eight months' follow-up. A smartphone app compared with NMI probably results in little to no difference in changes in total activity time at 12 months' follow-up and leisure time physical activity at 24 months' follow-up. Anthropometric measures: a smartphone app compared with NMI may reduce BMI (MD of BMI change -2.6 kg/m2, 95% CI -6 to 0.8; 2 studies, 146 participants; very low-certainty evidence) at six to eight months' follow-up, but the evidence is very uncertain. At 12 months' follow-up, a smartphone app probably resulted in little to no difference in BMI change (MD -0.1 kg/m2, 95% CI -0.4 to 0.3; 1 study; 650 participants; moderate-certainty evidence). A smartphone app compared with NMI may result in little to no difference in body weight change (MD -2.5 kg, 95% CI -6.8 to 1.7; 3 studies, 1044 participants; low-certainty evidence) at 12 months' follow-up. At 24 months' follow-up, a smartphone app probably resulted in little to no difference in body weight change (MD 0.7 kg, 95% CI -1.2 to 2.6; 1 study, 245 participants; moderate-certainty evidence). A smartphone app compared with NMI may result in little to no difference in self-efficacy for a physical activity score at eight months' follow-up, but the results are very uncertain. A smartphone app probably results in little to no difference in quality of life and well-being at 12 months (moderate-certainty evidence) and in little to no difference in various measures used to inform dietary behaviour at 12 and 24 months' follow-up. We are very uncertain about adverse events, which were only reported narratively in two studies (very low-certainty evidence). Smartphone app versus another smartphone app Two studies compared different versions of the same app in adults, showing no or minimal differences in outcomes. One study in adults compared two different apps (calorie counting versus ketogenic diet) and suggested a slight reduction in body weight at six months in favour of the ketogenic diet app. No studies were available for adolescents. Smartphone app versus personal coaching Only one study compared a smartphone app with personal coaching in adults, presenting data at three months. Two studies compared these interventions in adolescents. A smartphone app resulted in little to no difference in BMI z-score compared to personal coaching at six months' follow-up (MD 0, 95% CI -0.2 to 0.2; 1 study; 107 participants). Smartphone app versus usual care Only one study compared an app with usual care in adults but only reported data at three months on participant satisfaction. No studies were available for adolescents. We identified 34 ongoing studies. AUTHORS' CONCLUSIONS The available evidence is limited and does not demonstrate a clear benefit of smartphone applications as interventions for adolescents or adults with overweight or obesity. While the number of studies is growing, the evidence remains incomplete due to the high variability of the apps' features, content and components, which complicates direct comparisons and assessment of their effectiveness. Comparisons with either no or minimal intervention or personal coaching show minor effects, which are mostly not clinically significant. Minimal data for adolescents also warrants further research. Evidence is also scarce for low- and middle-income countries as well as for people with different socio-economic and cultural backgrounds. The 34 ongoing studies suggest sustained interest in the topic, with new evidence expected to emerge within the next two years. In practice, clinicians and healthcare practitioners should carefully consider the potential benefits, limitations, and evolving research when recommending smartphone apps to adolescents and adults with overweight or obesity.
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Affiliation(s)
- Maria-Inti Metzendorf
- Institute of General Practice, Medical Faculty of the Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - L Susan Wieland
- Center for Integrative Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Bernd Richter
- Institute of General Practice, Medical Faculty of the Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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Wang W, Ruan H, Shen Y, Cheng J, Sun W, Huang C. Effectiveness of utilizing step-monitoring devices to prevent and treat obesity in children and adolescents: A systematic review and meta-analysis. Digit Health 2024; 10:20552076241272589. [PMID: 39148809 PMCID: PMC11325471 DOI: 10.1177/20552076241272589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Accepted: 07/18/2024] [Indexed: 08/17/2024] Open
Abstract
Background Children and adolescents who are overweight and obese represent a growing public health issue. The use of step-monitoring devices as an intervention tool may be a simple, cost-effective, and easily replicable solution for addressing obesity in children and adolescents. No prior systematic reviews have evaluated the effectiveness of utilizing step-monitoring devices as an intervention method for obesity in children and adolescents. Methods Previous studies on using step-monitoring devices to prevent and treat obesity in children and adolescents were identified in the following databases: Web of Science, EMBASE, PubMed, Cochrane Library, SPORTDiscus, and SCOPUS. The search period for each database ranged from the year of their inception to 8 March 2023 (updated in June 2024). Meta-analyses were performed for mean differences (MDs) in body mass index (BMI), BMI z-score (BMI-Z), body fat, waist circumference, and body weight. Results From 12,907 relevant records, 23 studies were included in this meta-analysis. The included studies were mainly at low risk of bias, except for blinding. Step-monitoring device-based interventions had significant effects in reducing BMI-Z (MD -0.06; 95% CI -0.10 to -0.02), body fat (MD -0.95%; 95% CI -1.35 to -0.54), and body weight (MD -1.23 kg; 95% CI -2.36 to -0.10). However, there was no significant effect on BMI (MD -0.16 kg/m2; 95% CI -0.55 to 0.22) and waist circumference (MD -0.33 cm; 95% CI -1.23 to 0.58). Subgroup analyses indicated that participants who were overweight or obese showed greater intervention effects on BMI and BMI-Z compared to participants with normal weight. The programs with an intervention duration of ≤6 months presented a greater intervention effect on BMI-Z than those with an intervention duration of more than 6 months. The programs that established goals had a greater intervention effect on body fat than those that did not. Conclusions Step-monitoring devices may be an effective and generalizable intervention tool for the prevention and treatment of obesity in children and adolescents. Future studies should further explore how to set step goals and the duration of interventions to achieve better intervention effects.
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Affiliation(s)
- Wentao Wang
- Department of Basic Education, Zhejiang Tongji Vocational College of Science and Technology, Hangzhou, China
- Department of Sports and Exercise Science, Zhejiang University, Hangzhou, China
| | - Hongfang Ruan
- Department of Basic Education, Zhejiang Tongji Vocational College of Science and Technology, Hangzhou, China
| | - Yi Shen
- Department of Basic Education, Zhejiang Tongji Vocational College of Science and Technology, Hangzhou, China
| | - Jing Cheng
- Department of Basic Education, Zhejiang Tongji Vocational College of Science and Technology, Hangzhou, China
| | - Wei Sun
- Department of Military and Sports, Zhejiang University of Water Resources and Electric Power, Hangzhou, China
| | - Cong Huang
- Department of Sports and Exercise Science, Zhejiang University, Hangzhou, China
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Ollier J, Suryapalli P, Fleisch E, von Wangenheim F, Mair JL, Salamanca-Sanabria A, Kowatsch T. Can digital health researchers make a difference during the pandemic? Results of the single-arm, chatbot-led Elena+: Care for COVID-19 interventional study. Front Public Health 2023; 11:1185702. [PMID: 37693712 PMCID: PMC10485275 DOI: 10.3389/fpubh.2023.1185702] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 07/24/2023] [Indexed: 09/12/2023] Open
Abstract
Background The current paper details findings from Elena+: Care for COVID-19, an app developed to tackle the collateral damage of lockdowns and social distancing, by offering pandemic lifestyle coaching across seven health areas: anxiety, loneliness, mental resources, sleep, diet and nutrition, physical activity, and COVID-19 information. Methods The Elena+ app functions as a single-arm interventional study, with participants recruited predominantly via social media. We used paired samples T-tests and within subjects ANOVA to examine changes in health outcome assessments and user experience evaluations over time. To investigate the mediating role of behavioral activation (i.e., users setting behavioral intentions and reporting actual behaviors) we use mixed-effect regression models. Free-text entries were analyzed qualitatively. Results Results show strong demand for publicly available lifestyle coaching during the pandemic, with total downloads (N = 7'135) and 55.8% of downloaders opening the app (n = 3,928) with 9.8% completing at least one subtopic (n = 698). Greatest areas of health vulnerability as assessed with screening measures were physical activity with 62% (n = 1,000) and anxiety with 46.5% (n = 760). The app was effective in the treatment of mental health; with a significant decrease in depression between first (14 days), second (28 days), and third (42 days) assessments: F2,38 = 7.01, p = 0.003, with a large effect size (η2G = 0.14), and anxiety between first and second assessments: t54 = 3.7, p = <0.001 with a medium effect size (Cohen d = 0.499). Those that followed the coaching program increased in net promoter score between the first and second assessment: t36 = 2.08, p = 0.045 with a small to medium effect size (Cohen d = 0.342). Mediation analyses showed that while increasing number of subtopics completed increased behavioral activation (i.e., match between behavioral intentions and self-reported actual behaviors), behavioral activation did not mediate the relationship to improvements in health outcome assessments. Conclusions Findings show that: (i) there is public demand for chatbot led digital coaching, (ii) such tools can be effective in delivering treatment success, and (iii) they are highly valued by their long-term user base. As the current intervention was developed at rapid speed to meet the emergency pandemic context, the future looks bright for other public health focused chatbot-led digital health interventions.
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Affiliation(s)
- Joseph Ollier
- Mobiliar Lab for Analytics, Chair of Technology Marketing, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
| | - Pavani Suryapalli
- Centre for Digital Health Interventions, Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland
| | - Elgar Fleisch
- Centre for Digital Health Interventions, Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland
- Centre for Digital Health Interventions, Chair of Information Management, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
| | - Florian von Wangenheim
- Mobiliar Lab for Analytics, Chair of Technology Marketing, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
- Centre for Digital Health Interventions, Chair of Information Management, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
| | - Jacqueline Louise Mair
- Centre for Digital Health Interventions, Chair of Information Management, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Alicia Salamanca-Sanabria
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (ASTAR), Singapore, Singapore
| | - Tobias Kowatsch
- Centre for Digital Health Interventions, Chair of Information Management, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- School of Medicine, University of St. Gallen, St. Gallen, Switzerland
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Castro O, Mair JL, Salamanca-Sanabria A, Alattas A, Keller R, Zheng S, Jabir A, Lin X, Frese BF, Lim CS, Santhanam P, van Dam RM, Car J, Lee J, Tai ES, Fleisch E, von Wangenheim F, Tudor Car L, Müller-Riemenschneider F, Kowatsch T. Development of "LvL UP 1.0": a smartphone-based, conversational agent-delivered holistic lifestyle intervention for the prevention of non-communicable diseases and common mental disorders. Front Digit Health 2023; 5:1039171. [PMID: 37234382 PMCID: PMC10207359 DOI: 10.3389/fdgth.2023.1039171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 04/06/2023] [Indexed: 05/28/2023] Open
Abstract
Background Non-communicable diseases (NCDs) and common mental disorders (CMDs) are the leading causes of death and disability worldwide. Lifestyle interventions via mobile apps and conversational agents present themselves as low-cost, scalable solutions to prevent these conditions. This paper describes the rationale for, and development of, "LvL UP 1.0″, a smartphone-based lifestyle intervention aimed at preventing NCDs and CMDs. Materials and Methods A multidisciplinary team led the intervention design process of LvL UP 1.0, involving four phases: (i) preliminary research (stakeholder consultations, systematic market reviews), (ii) selecting intervention components and developing the conceptual model, (iii) whiteboarding and prototype design, and (iv) testing and refinement. The Multiphase Optimization Strategy and the UK Medical Research Council framework for developing and evaluating complex interventions were used to guide the intervention development. Results Preliminary research highlighted the importance of targeting holistic wellbeing (i.e., both physical and mental health). Accordingly, the first version of LvL UP features a scalable, smartphone-based, and conversational agent-delivered holistic lifestyle intervention built around three pillars: Move More (physical activity), Eat Well (nutrition and healthy eating), and Stress Less (emotional regulation and wellbeing). Intervention components include health literacy and psychoeducational coaching sessions, daily "Life Hacks" (healthy activity suggestions), breathing exercises, and journaling. In addition to the intervention components, formative research also stressed the need to introduce engagement-specific components to maximise uptake and long-term use. LvL UP includes a motivational interviewing and storytelling approach to deliver the coaching sessions, as well as progress feedback and gamification. Offline materials are also offered to allow users access to essential intervention content without needing a mobile device. Conclusions The development process of LvL UP 1.0 led to an evidence-based and user-informed smartphone-based intervention aimed at preventing NCDs and CMDs. LvL UP is designed to be a scalable, engaging, prevention-oriented, holistic intervention for adults at risk of NCDs and CMDs. A feasibility study, and subsequent optimisation and randomised-controlled trials are planned to further refine the intervention and establish effectiveness. The development process described here may prove helpful to other intervention developers.
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Affiliation(s)
- Oscar Castro
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
| | - Jacqueline Louise Mair
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Alicia Salamanca-Sanabria
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
| | - Aishah Alattas
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
| | - Roman Keller
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Shenglin Zheng
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Ahmad Jabir
- Neuroscience and Mental Health, Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, Singapore
| | - Xiaowen Lin
- Neuroscience and Mental Health, Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, Singapore
| | - Bea Franziska Frese
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Centre for Digital Health Interventions,Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland
| | - Chang Siang Lim
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
| | - Prabhakaran Santhanam
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
| | - Rob M. van Dam
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- Department of Exercise and Nutrition Sciences, Milken Institute School of Public Health, George Washington University, Washington DC, DC, United States
| | - Josip Car
- Centre for Population Health Sciences, LKCMedicine, Nanyang Technological University, Singapore, Singapore
- Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Jimmy Lee
- Neuroscience and Mental Health, Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, Singapore
- Research Division, Institute of Mental Health, Singapore, Singapore
- North Region & Department of Psychosis, Institute of Mental Health, Singapore, Singapore
| | - E Shyong Tai
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Elgar Fleisch
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Centre for Digital Health Interventions,Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland
| | - Florian von Wangenheim
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
| | - Lorainne Tudor Car
- Neuroscience and Mental Health, Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, Singapore
- Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Falk Müller-Riemenschneider
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- Digital Health Center, Berlin Institute of Health, Charite University Medical Centre Berlin, Berlin, Germany
| | - Tobias Kowatsch
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- School of Medicine, University of St. Gallen, St. Gallen, Switzerland
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Han R, Todd A, Wardak S, Partridge SR, Raeside R. Feasibility and Acceptability of Chatbots for Nutrition and Physical Activity Health Promotion Among Adolescents: Systematic Scoping Review With Adolescent Consultation. JMIR Hum Factors 2023; 10:e43227. [PMID: 37145858 PMCID: PMC10199392 DOI: 10.2196/43227] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 03/15/2023] [Accepted: 04/13/2023] [Indexed: 05/06/2023] Open
Abstract
BACKGROUND Reducing lifestyle risk behaviors among adolescents depends on access to age-appropriate health promotion information. Chatbots-computer programs designed to simulate conversations with human users-have the potential to deliver health information to adolescents to improve their lifestyle behaviors and support behavior change, but research on the feasibility and acceptability of chatbots in the adolescent population is unknown. OBJECTIVE This systematic scoping review aims to evaluate the feasibility and acceptability of chatbots in nutrition and physical activity interventions among adolescents. A secondary aim is to consult adolescents to identify features of chatbots that are acceptable and feasible. METHODS We searched 6 electronic databases from March to April 2022 (MEDLINE, Embase, Joanna Briggs Institute, the Cumulative Index to Nursing and Allied Health, the Association for Computing Machinery library, and the IT database Institute of Electrical and Electronics Engineers). Peer-reviewed studies were included that were conducted in the adolescent population (10-19 years old) without any chronic disease, except obesity or type 2 diabetes, and assessed chatbots used nutrition or physical activity interventions or both that encouraged individuals to meet dietary or physical activity guidelines and support positive behavior change. Studies were screened by 2 independent reviewers, with any queries resolved by a third reviewer. Data were extracted into tables and collated in a narrative summary. Gray literature searches were also undertaken. Results of the scoping review were presented to a diverse youth advisory group (N=16, 13-18 years old) to gain insights into this topic beyond what is published in the literature. RESULTS The search identified 5558 papers, with 5 (0.1%) studies describing 5 chatbots meeting the inclusion criteria. The 5 chatbots were supported by mobile apps using a combination of the following features: personalized feedback, conversational agents, gamification, and monitoring of behavior change. Of the 5 studies, 2 (40.0%) studies focused on nutrition, 2 (40.0%) studies focused on physical activity, and 1 (20.0%) focused on both nutrition and physical activity. Feasibility and acceptability varied across the 5 studies, with usage rates above 50% in 3 (60.0%) studies. In addition, 3 (60.0%) studies reported health-related outcomes, with only 1 (20.0%) study showing promising effects of the intervention. Adolescents presented novel concerns around the use of chatbots in nutrition and physical activity interventions, including ethical concerns and the use of false or misleading information. CONCLUSIONS Limited research is available on chatbots in adolescent nutrition and physical activity interventions, finding insufficient evidence on the acceptability and feasibility of chatbots in the adolescent population. Similarly, adolescent consultation identified issues in the design features that have not been mentioned in the published literature. Therefore, chatbot codesign with adolescents may help ensure that such technology is feasible and acceptable to an adolescent population.
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Affiliation(s)
- Rui Han
- Engagement and Co-Design Research Hub, School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Westmead, Australia
| | - Allyson Todd
- Engagement and Co-Design Research Hub, School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Westmead, Australia
| | - Sara Wardak
- Engagement and Co-Design Research Hub, School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Westmead, Australia
| | - Stephanie R Partridge
- Engagement and Co-Design Research Hub, School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Westmead, Australia
| | - Rebecca Raeside
- Engagement and Co-Design Research Hub, School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Westmead, Australia
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Pervanidou P, Chatzidaki E, Nicolaides NC, Voutetakis A, Polychronaki N, Chioti V, Kitani RA, Kyrkopoulou E, Zarkogianni K, Kalafatis E, Mitsis K, Perakis Κ, Nikita K, Kanaka-Gantenbein C. The Impact of the ENDORSE Digital Weight Management Program on the Metabolic Profile of Children and Adolescents with Overweight and Obesity and on Food Parenting Practices. Nutrients 2023; 15:nu15071777. [PMID: 37049618 PMCID: PMC10097404 DOI: 10.3390/nu15071777] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 03/29/2023] [Accepted: 04/03/2023] [Indexed: 04/09/2023] Open
Abstract
Childhood obesity is a serious public health problem worldwide. The ENDORSE platform is an innovative software ecosystem based on Artificial Intelligence which consists of mobile applications for parents and health professionals, activity trackers, and mobile games for children. This study explores the impact of the ENDORSE platform on metabolic parameters associated with pediatric obesity and on the food parenting practices of the participating mothers. Therefore, the metabolic parameters of the 45 children (mean age: 10.42 years, 53% girls, 58% pubertal, mean baseline BMI z-score 2.83) who completed the ENDORSE study were evaluated. The Comprehensive Feeding Practices Questionnaire was used for the assessment of food parenting practices. Furthermore, regression analysis was used to investigate possible associations between BMI z-score changes and changes in metabolic parameters and food parenting practices. Overall, there was a statistically significant reduction in glycated hemoglobin (mean change = −0.10, p = 0.013), SGOT (mean change = −1.84, p = 0.011), and SGPT (mean change = −2.95, p = 0.022). Emotional feeding/food as reward decreased (mean change −0.21, p = 0.007) and healthy eating guidance increased (mean change = 0.11, p = 0.051). Linear regression analysis revealed that BMI z-score change had a robust and significant correlation with important metabolic parameters: HOMA-IR change (beta coefficient = 3.60, p-value = 0.046), SGPT change (beta coefficient = 11.90, p-value = 0.037), and cortisol change (beta coefficient = 9.96, p-value = 0.008). Furthermore, healthy eating guidance change had a robust negative relationship with BMI z-score change (beta coefficient = −0.29, p-value = 0.007). Conclusions: The Endorse digital weight management program improved several metabolic parameters and food parenting practices.
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Affiliation(s)
- Panagiota Pervanidou
- First Department of Pediatrics, School of Medicine, National and Kapodistrian University of Athens, Aghia Sophia Children’s Hospital, 11527 Athens, Greece
| | - Evi Chatzidaki
- First Department of Pediatrics, School of Medicine, National and Kapodistrian University of Athens, Aghia Sophia Children’s Hospital, 11527 Athens, Greece
| | - Nicolas C. Nicolaides
- First Department of Pediatrics, School of Medicine, National and Kapodistrian University of Athens, Aghia Sophia Children’s Hospital, 11527 Athens, Greece
| | - Antonis Voutetakis
- Department of Pediatrics, School of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece
| | - Nektaria Polychronaki
- First Department of Pediatrics, School of Medicine, National and Kapodistrian University of Athens, Aghia Sophia Children’s Hospital, 11527 Athens, Greece
| | - Vassiliki Chioti
- First Department of Pediatrics, School of Medicine, National and Kapodistrian University of Athens, Aghia Sophia Children’s Hospital, 11527 Athens, Greece
| | - Rosa-Anna Kitani
- First Department of Pediatrics, School of Medicine, National and Kapodistrian University of Athens, Aghia Sophia Children’s Hospital, 11527 Athens, Greece
| | - Eleni Kyrkopoulou
- Department of Economics, University of Piraeus, 18534 Pireas, Greece
| | - Konstantia Zarkogianni
- School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece
| | - Eleftherios Kalafatis
- School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece
| | - Kostas Mitsis
- School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece
| | | | - Konstantina Nikita
- School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece
| | - Christina Kanaka-Gantenbein
- First Department of Pediatrics, School of Medicine, National and Kapodistrian University of Athens, Aghia Sophia Children’s Hospital, 11527 Athens, Greece
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12
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Vidmar AP, Salvy SJ, Wee CP, Pretlow R, Fox DS, Yee JK, Garell C, Glasner S, Mittelman SD. An addiction-based digital weight loss intervention: A multi-centre randomized controlled trial. Pediatr Obes 2023; 18:e12990. [PMID: 36484235 PMCID: PMC9905275 DOI: 10.1111/ijpo.12990] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 10/07/2022] [Accepted: 11/02/2022] [Indexed: 12/13/2022]
Abstract
OBJECTIVE This randomized clinical trial tested the effectiveness of an addiction-based digital weight-loss intervention, focusing on withdrawal/abstinence from self-identified problem foods, snacking and excessive amounts at meals, and discomfort displacement, with and without coaching, compared to an in-person, multi-disciplinary, care model among adolescents with obesity. We hypothesized that the digital intervention with coaching would yield greater weight loss and lower delivery burden than the standard clinical arm, and greater participant engagement than the digital arm without coaching. METHODS Adolescents were randomized to app intervention, with or without coaching, or in-person multidisciplinary obesity intervention for 6 months. The primary outcome was change in %BMIp95 at weeks 12 and 24. A mixed-effects linear regression model was used to assess the association between change in %BMIp95 and intervention arm. We were also interested in assessing delivery burden, participant engagement and evaluating the relationships between weight change and demographic characteristics, mood, executive function and eating behaviours. RESULTS All adolescents (n = 161; BMI ≥95th%, age 16 ± 2.5 year; 47% Hispanic, 65% female, 59% publicly insured) lost weight over 24-weeks (-1.29%, [-1.82, -0.76], p < 0.0001), with no significant weight loss difference between groups (p = 0.3). Girls lost more weight than boys, whereas binge eating behaviour at baseline was associated with increase in %BMIp95 when controlling for other covariates. There was no association between ethnicity, mood, timing of intervention in relation to the pandemic, or executive function and change in %BMIp95 . CONCLUSIONS Contrary with our hypothesis, our results showed no difference in the change in BMI status between treatment arms. Since efficacy of this digital intervention was not inferior to in-person, multi-disciplinary care, this could offer a reasonable weight management option for clinicians, based on youth and family specific characteristics, such as accessibility, resources, and communication styles. CLINICAL TRIAL REGISTRATION ClinicalTrials.gov identifier: NCT035008353.
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Affiliation(s)
- Alaina P. Vidmar
- Department of Pediatrics, Center for Endocrinology, Diabetes and Metabolism, Children’s Hospital Los Angeles and Keck School of Medicine of USC, Los Angeles, California, USA
| | - Sarah J. Salvy
- Department of Medicine, Cedars-Sinai Medical Center, Research Center for Health Equity Samuel Oschin Comprehensive Cancer Institute, Los Angeles, California, USA
| | - Choo Phei Wee
- Department of Population and Public Health Sciences, Keck School of Medicine of USC, Southern California Clinical Science Institute, Los Angeles, California, USA
| | | | - D. Steven Fox
- Department of Pharmaceutical and Health Economics, School of Pharmacy of the University of Southern California, Los Angeles, California, USA
| | - Jennifer K. Yee
- Division of Pediatric Endocrinology, Harbor-UCLA Medical Center, Harbor-UCLA Medical Center, Torrance, California, United States
| | - Cambria Garell
- Department of Pediatrics, Division of General Pediatrics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Suzette Glasner
- Department of Psychiatry & Biobehavioral Sciences, University of California, Los Angeles, California, USA
| | - Steven D. Mittelman
- Department of Pediatrics, Division of General Pediatrics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
- Department of Pediatrics, Division of Endocrinology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
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Salas-Groves E, Galyean S, Alcorn M, Childress A. Behavior Change Effectiveness Using Nutrition Apps in People With Chronic Diseases: Scoping Review. JMIR Mhealth Uhealth 2023; 11:e41235. [PMID: 36637888 PMCID: PMC9883741 DOI: 10.2196/41235] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 11/15/2022] [Accepted: 11/30/2022] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Cardiovascular disease, cancer, diabetes mellitus, and obesity are common chronic diseases, and their prevalence is reaching an epidemic level worldwide. As the impact of chronic diseases continues to increase, finding strategies to improve care, access to care, and patient empowerment becomes increasingly essential. Health care providers use mobile health (mHealth) to access clinical information, collaborate with care teams, communicate over long distances with patients, and facilitate real-time monitoring and interventions. However, these apps focus on improving general health care concerns, with limited apps focusing on specific chronic diseases and the nutrition involved in the disease state. Hence, available evidence on the effectiveness of mHealth apps toward behavior change to improve chronic disease outcomes is limited. OBJECTIVE The objective of this scoping review was to provide an overview of behavior change effectiveness using mHealth nutrition interventions in people with chronic diseases (ie, cardiovascular disease, diabetes mellitus, cancer, and obesity). We further evaluated the behavior change techniques and theories or models used for behavior change, if any. METHODS A scoping review was conducted through a systematic literature search in the MEDLINE, EBSCO, PubMed, ScienceDirect, and Scopus databases. Studies were excluded from the review if they did not involve an app or nutrition intervention, were written in a language other than English, were duplicates from other database searches, or were literature reviews. Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines, the systematic review process included 4 steps: identification of records through the database search, screening of duplicate and excluded records, eligibility assessment of full-text records, and final analysis of included records. RESULTS In total, 46 studies comprising 256,430 patients were included. There was diversity in the chronic disease state, study design, number of participants, in-app features, behavior change techniques, and behavior models used in the studies. In addition, our review found that less than half (19/46, 41%) of the studies based their nutrition apps on a behavioral theory or its constructs. Of the 46 studies, 11 (24%) measured maintenance of health behavior change, of which 7 (64%) sustained behavior change for approximately 6 to 12 months and 4 (36%) showed a decline in behavior change or discontinued app use. CONCLUSIONS The results suggest that mHealth apps involving nutrition can significantly improve health outcomes in people with chronic diseases. Tailoring nutrition apps to specific populations is recommended for effective behavior change and improvement of health outcomes. In addition, some studies (7/46, 15%) showed sustained health behavior change, and some (4/46, 9%) showed a decline in the use of nutrition apps. These results indicate a need for further investigation on the sustainability of the health behavior change effectiveness of disease-specific nutrition apps.
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Affiliation(s)
- Emily Salas-Groves
- Department of Nutritional Sciences, Texas Tech University, Lubbock, TX, United States
| | - Shannon Galyean
- Department of Nutritional Sciences, Texas Tech University, Lubbock, TX, United States
| | - Michelle Alcorn
- Department of Hospitality & Retail Management, Texas Tech University, Lubbock, TX, United States
| | - Allison Childress
- Department of Nutritional Sciences, Texas Tech University, Lubbock, TX, United States
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Dhinagaran DA, Martinengo L, Ho MHR, Joty S, Kowatsch T, Atun R, Tudor Car L. Designing, Developing, Evaluating, and Implementing a Smartphone-Delivered, Rule-Based Conversational Agent (DISCOVER): Development of a Conceptual Framework. JMIR Mhealth Uhealth 2022; 10:e38740. [PMID: 36194462 PMCID: PMC9579935 DOI: 10.2196/38740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 08/02/2022] [Accepted: 08/26/2022] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Conversational agents (CAs), also known as chatbots, are computer programs that simulate human conversations by using predetermined rule-based responses or artificial intelligence algorithms. They are increasingly used in health care, particularly via smartphones. There is, at present, no conceptual framework guiding the development of smartphone-based, rule-based CAs in health care. To fill this gap, we propose structured and tailored guidance for their design, development, evaluation, and implementation. OBJECTIVE The aim of this study was to develop a conceptual framework for the design, evaluation, and implementation of smartphone-delivered, rule-based, goal-oriented, and text-based CAs for health care. METHODS We followed the approach by Jabareen, which was based on the grounded theory method, to develop this conceptual framework. We performed 2 literature reviews focusing on health care CAs and conceptual frameworks for the development of mobile health interventions. We identified, named, categorized, integrated, and synthesized the information retrieved from the literature reviews to develop the conceptual framework. We then applied this framework by developing a CA and testing it in a feasibility study. RESULTS The Designing, Developing, Evaluating, and Implementing a Smartphone-Delivered, Rule-Based Conversational Agent (DISCOVER) conceptual framework includes 8 iterative steps grouped into 3 stages, as follows: design, comprising defining the goal, creating an identity, assembling the team, and selecting the delivery interface; development, including developing the content and building the conversation flow; and the evaluation and implementation of the CA. They were complemented by 2 cross-cutting considerations-user-centered design and privacy and security-that were relevant at all stages. This conceptual framework was successfully applied in the development of a CA to support lifestyle changes and prevent type 2 diabetes. CONCLUSIONS Drawing on published evidence, the DISCOVER conceptual framework provides a step-by-step guide for developing rule-based, smartphone-delivered CAs. Further evaluation of this framework in diverse health care areas and settings and for a variety of users is needed to demonstrate its validity. Future research should aim to explore the use of CAs to deliver health care interventions, including behavior change and potential privacy and safety concerns.
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Affiliation(s)
| | - Laura Martinengo
- Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, Singapore
| | - Moon-Ho Ringo Ho
- School of Social Sciences, Nanyang Technological University Singapore, Singapore, Singapore
| | - Shafiq Joty
- School of Computer Sciences and Engineering, Nanyang Technological University Singapore, Singapore, Singapore
| | - Tobias Kowatsch
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- School of Medicine, University of St Gallen, St Gallen, Switzerland
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
- Future Health Technologies Programme, Campus for Research Excellence and Technological Enterprise, Singapore-ETH Centre, Singapore, Singapore
| | - Rifat Atun
- Department of Global Health & Population, Department of Health Policy & Management, Harvard TH Chan School of Public Health, Harvard University, Cambridge, MA, United States
- Department of Global Health and Social Medicine, Harvard Medical School, Harvard University, Cambridge, MA, United States
- Health Systems Innovation Lab, Harvard TH Chan School of Public Health, Harvard University, Cambridge, MA, United States
| | - Lorainne Tudor Car
- Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, Singapore
- Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, United Kingdom
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15
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Nißen M, Rüegger D, Stieger M, Flückiger C, Allemand M, V Wangenheim F, Kowatsch T. The Effects of Health Care Chatbot Personas With Different Social Roles on the Client-Chatbot Bond and Usage Intentions: Development of a Design Codebook and Web-Based Study. J Med Internet Res 2022; 24:e32630. [PMID: 35475761 PMCID: PMC9096656 DOI: 10.2196/32630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 01/21/2022] [Accepted: 02/17/2022] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND The working alliance refers to an important relationship quality between health professionals and clients that robustly links to treatment success. Recent research shows that clients can develop an affective bond with chatbots. However, few research studies have investigated whether this perceived relationship is affected by the social roles of differing closeness a chatbot can impersonate and by allowing users to choose the social role of a chatbot. OBJECTIVE This study aimed at understanding how the social role of a chatbot can be expressed using a set of interpersonal closeness cues and examining how these social roles affect clients' experiences and the development of an affective bond with the chatbot, depending on clients' characteristics (ie, age and gender) and whether they can freely choose a chatbot's social role. METHODS Informed by the social role theory and the social response theory, we developed a design codebook for chatbots with different social roles along an interpersonal closeness continuum. Based on this codebook, we manipulated a fictitious health care chatbot to impersonate one of four distinct social roles common in health care settings-institution, expert, peer, and dialogical self-and examined effects on perceived affective bond and usage intentions in a web-based lab study. The study included a total of 251 participants, whose mean age was 41.15 (SD 13.87) years; 57.0% (143/251) of the participants were female. Participants were either randomly assigned to one of the chatbot conditions (no choice: n=202, 80.5%) or could freely choose to interact with one of these chatbot personas (free choice: n=49, 19.5%). Separate multivariate analyses of variance were performed to analyze differences (1) between the chatbot personas within the no-choice group and (2) between the no-choice and the free-choice groups. RESULTS While the main effect of the chatbot persona on affective bond and usage intentions was insignificant (P=.87), we found differences based on participants' demographic profiles: main effects for gender (P=.04, ηp2=0.115) and age (P<.001, ηp2=0.192) and a significant interaction effect of persona and age (P=.01, ηp2=0.102). Participants younger than 40 years reported higher scores for affective bond and usage intentions for the interpersonally more distant expert and institution chatbots; participants 40 years or older reported higher outcomes for the closer peer and dialogical-self chatbots. The option to freely choose a persona significantly benefited perceptions of the peer chatbot further (eg, free-choice group affective bond: mean 5.28, SD 0.89; no-choice group affective bond: mean 4.54, SD 1.10; P=.003, ηp2=0.117). CONCLUSIONS Manipulating a chatbot's social role is a possible avenue for health care chatbot designers to tailor clients' chatbot experiences using user-specific demographic factors and to improve clients' perceptions and behavioral intentions toward the chatbot. Our results also emphasize the benefits of letting clients freely choose between chatbots.
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Affiliation(s)
- Marcia Nißen
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
| | - Dominik Rüegger
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
- Pathmate Technologies AG, Zurich, Switzerland
| | - Mirjam Stieger
- Department of Psychology, Brandeis University, Waltham, MA, United States
- Institute of Communication and Marketing, Lucerne University of Applied Sciences and Arts, Lucerne, Switzerland
- Department of Psychology, University of Zurich, Zurich, Switzerland
| | | | - Mathias Allemand
- Department of Psychology, University of Zurich, Zurich, Switzerland
- University Research Priority Programs, Dynamics of Healthy Aging, University of Zurich, Zurich, Switzerland
| | - Florian V Wangenheim
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
| | - Tobias Kowatsch
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
- Centre for Digital Health Interventions, Institute of Technology Management, University of St.Gallen, St.Gallen, Switzerland
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16
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Chew HSJ. The Use of Artificial Intelligence-Based Conversational Agents (Chatbots) for Weight Loss: Scoping Review and Practical Recommendations. JMIR Med Inform 2022; 10:e32578. [PMID: 35416791 PMCID: PMC9047740 DOI: 10.2196/32578] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 10/04/2021] [Accepted: 01/08/2022] [Indexed: 12/31/2022] Open
Abstract
Background Overweight and obesity have now reached a state of a pandemic despite the clinical and commercial programs available. Artificial intelligence (AI) chatbots have a strong potential in optimizing such programs for weight loss. Objective This study aimed to review AI chatbot use cases for weight loss and to identify the essential components for prolonging user engagement. Methods A scoping review was conducted using the 5-stage framework by Arksey and O’Malley. Articles were searched across nine electronic databases (ACM Digital Library, CINAHL, Cochrane Central, Embase, IEEE Xplore, PsycINFO, PubMed, Scopus, and Web of Science) until July 9, 2021. Gray literature, reference lists, and Google Scholar were also searched. Results A total of 23 studies with 2231 participants were included and evaluated in this review. Most studies (8/23, 35%) focused on using AI chatbots to promote both a healthy diet and exercise, 13% (3/23) of the studies used AI chatbots solely for lifestyle data collection and obesity risk assessment whereas only 4% (1/23) of the studies focused on promoting a combination of a healthy diet, exercise, and stress management. In total, 48% (11/23) of the studies used only text-based AI chatbots, 52% (12/23) operationalized AI chatbots through smartphones, and 39% (9/23) integrated data collected through fitness wearables or Internet of Things appliances. The core functions of AI chatbots were to provide personalized recommendations (20/23, 87%), motivational messages (18/23, 78%), gamification (6/23, 26%), and emotional support (6/23, 26%). Study participants who experienced speech- and augmented reality–based chatbot interactions in addition to text-based chatbot interactions reported higher user engagement because of the convenience of hands-free interactions. Enabling conversations through multiple platforms (eg, SMS text messaging, Slack, Telegram, Signal, WhatsApp, or Facebook Messenger) and devices (eg, laptops, Google Home, and Amazon Alexa) was reported to increase user engagement. The human semblance of chatbots through verbal and nonverbal cues improved user engagement through interactivity and empathy. Other techniques used in text-based chatbots included personally and culturally appropriate colloquial tones and content; emojis that emulate human emotional expressions; positively framed words; citations of credible information sources; personification; validation; and the provision of real-time, fast, and reliable recommendations. Prevailing issues included privacy; accountability; user burden; and interoperability with other databases, third-party applications, social media platforms, devices, and appliances. Conclusions AI chatbots should be designed to be human-like, personalized, contextualized, immersive, and enjoyable to enhance user experience, engagement, behavior change, and weight loss. These require the integration of health metrics (eg, based on self-reports and wearable trackers), personality and preferences (eg, based on goal achievements), circumstantial behaviors (eg, trigger-based overconsumption), and emotional states (eg, chatbot conversations and wearable stress detectors) to deliver personalized and effective recommendations for weight loss.
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Affiliation(s)
- Han Shi Jocelyn Chew
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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17
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Pawellek S, Ziegeldorf A, Wulff H. [Strategies and effects of digital interventions in overweight and obesity treatments in children and adolescents-a systematic review]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2022; 65:624-634. [PMID: 35320378 PMCID: PMC9064867 DOI: 10.1007/s00103-022-03512-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 02/18/2022] [Indexed: 11/24/2022]
Abstract
BACKGROUND Rising obesity prevalence in childhood and adolescence are characterized by unhealthy lifestyles such as low physical activity due to high media use. Recent studies use the accessibility of this target group through digital media, whereby technologies represent new approaches in the intervention design of weight reduction. However, the question arises as to which digital combinations and methodological program concepts condition effective body mass index (BMI) changes. AIM To gain insights into effective program design and media use; digital intervention strategies for BMI reduction in overweight and obese children and adolescents will be analyzed and evaluated. METHODS A systematic review was conducted in the databases MEDLINE via PubMed, Science Direct, and Web of Science to analyze studies from 2016 to 2021 on changes in BMI and BMI z‑score of overweight and obese 6‑ to 18-year-olds. Methodological study quality was assessed according to the Cochrane Risk of Bias guidelines. RESULTS From 3974 studies, seven articles describing the use of fitness wristbands, smartphones, and computer-based programs were identified. All media achieved BMI reductions, with smartphone interventions via calls and messages causing the most significant changes. DISCUSSION Smartphones as providers of digital programs (e.g., apps) offer effective approaches to obesity reduction. Based on the data, the selection and combination of several media as well as the relevance of family involvement and the methodological foundation of the measures are confirmed. Due to the young age of the participants, media interventions must be made accessible to the target group.
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Affiliation(s)
- Sabine Pawellek
- Institut für Gesundheitssport und Public Health, Universität Leipzig, Jahnallee 59, 04109, Leipzig, Deutschland.
| | - Alexandra Ziegeldorf
- Institut für Gesundheitssport und Public Health, Universität Leipzig, Jahnallee 59, 04109, Leipzig, Deutschland
| | - Hagen Wulff
- Gesundheitserziehung/Gesundheitsbildung, Universität Potsdam, Potsdam, Deutschland
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